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International Journal of Fluid Mechanics Research
Главный редактор: Atle Jensen (open in a new tab)
Заместитель главного редактора: Valery Oliynik (open in a new tab)
Редактор-основатель: Victor T. Grinchenko (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5102

ISSN Онлайн: 2152-5110

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.1 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0002 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.33 SJR: 0.256 SNIP: 0.49 CiteScore™:: 2.4 H-Index: 23

Indexed in

Model Researches of a Spectral-Correlation Characteristics of the Breath Noise in Human Respiratory Tract

Том 28, Выпуск 1&2, 2001, pp. 274-291
DOI: 10.1615/InterJFluidMechRes.v28.i1-2.200
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Краткое описание

The model researches of a spectral-correlation characteristics of the breath noise in human respiratory channel are held. The researches are conducted using the methods of statistical simulation. On the basis of acoustic model of a respiratory tract, offered in this paper, the estimations of a number of spectral-correlation characteristics of the breath noise are obtained, which are of importance for computer diagnostics of the lung diseases. The utility of the correlation co-processing for data, registered on a neck and thorax, is stated. It is shown, that the outcomes of such co-processing essentially depend on a correlation degree between the sources of the breath noise and on their size and position. The features, describing the correlation degree and the position of the noise sources, is determined. The obtained estimations of the spectral-correlation characteristics are compared with the experimental data. As the result, the best agreement between the model and the experimental data was demonstrated under the hypothesis about a noncorrelatedness of the breath noise sources.

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